MM-Eval unifies evaluation of multimodal summaries by integrating factual text quality, cross-modal relevance via MLLM judge, and visual diversity via truncated CLIP entropy, then calibrates their combination on human preferences.
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A systematic analysis of 284 manually reviewed papers plus 1.8k+ others from 2023-2025 reveals under-reporting of human evaluation study design details, creating ambiguity in what was measured and how.
SPeCTrA-Sum uses hierarchical cross-modal fusion via DVP and DPP-distilled image selection via VRP to generate more accurate and visually grounded multimodal summaries.
citing papers explorer
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Measuring What Matters Beyond Text: Evaluating Multimodal Summaries by Quality, Alignment, and Diversity
MM-Eval unifies evaluation of multimodal summaries by integrating factual text quality, cross-modal relevance via MLLM judge, and visual diversity via truncated CLIP entropy, then calibrates their combination on human preferences.
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Illusions of the Gold Standard: A Large-scale Analysis of Human Evaluation Protocols for Long-form Text Generation
A systematic analysis of 284 manually reviewed papers plus 1.8k+ others from 2023-2025 reveals under-reporting of human evaluation study design details, creating ambiguity in what was measured and how.
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Towards Visually Grounded Multimodal Summarization via Cross-Modal Transformer and Gated Attention
SPeCTrA-Sum uses hierarchical cross-modal fusion via DVP and DPP-distilled image selection via VRP to generate more accurate and visually grounded multimodal summaries.